nep-cbe New Economics Papers
on Cognitive and Behavioural Economics
Issue of 2023‒03‒06
four papers chosen by
Marco Novarese
Università degli Studi del Piemonte Orientale

  1. A Note on Salience of Own Preferences and the Consensus Effect By Thomas Dohmen; Simone Quercia; Jana Willrodt
  2. Mixture Attitudes of Expectation-Based Loss Aversion By Daijiro Kawanaka
  3. Through the Looking Glass: Transparency about Others' Luck and Effort Enhances Redistribution By Wiese, Juliane V.; Powdthavee, Nattavudh; Yeo, Jonathan; Riyanto, Yohanes E.
  4. BEAST-Net: Learning novel behavioral insights using a neural network adaptation of a behavioral model By Shoshan, Vered; Hazan, Tamir; Plonsky, Ori

  1. By: Thomas Dohmen (University of Bonn, IZA Institute of Labor Economics, Maastricht University); Simone Quercia (University of Verona); Jana Willrodt (Düsseldorf Institute for Competition Economics (DICE))
    Abstract: In this paper, we hypothesize that the strength of the consensus effect, i.e., the tendency for people to overweight the prevalence of their own values and preferences when forming beliefs about others’ values and preferences, depends on the salience of own preferences. We manipulate salience by varying the order of elicitation of preferences and beliefs. Although our results confirmthe existence of the consensus effect, we find no evidence of a difference between the two orders of elicitation. While our results highlight the robustness of the consensus effect, they also indicate that salience does not mediate the strength of this phenomenon.
    Keywords: Consensus effect, social preferences, trust game, beliefs
    JEL: C91 D01 D83 D91
    Date: 2023–02
  2. By: Daijiro Kawanaka (Graduate school of Osaka University)
    Abstract: Köszegi and Rabin (2006, 2007) formulate loss-aversion models called Preferred Personal Equilibrium (PPE) and Choice-acclimating Personal Equilibrium (CPE) so successfully that many papers have been applied their models to a variety of economic fields in this decade. In this paper, without assuming any specific functional form, I show that these two loss-aversion models satisfy strong mixture aversion in the sense that a non-degenerate lottery is strictly preferred to any mixture between the lottery and its certainty equivalent. This property distinguishes these loss-aversion models from many other standard models such as the expected utility theory and disappointment aversion so that this result allows experimental economists to test a class of these models in a simple setting.
    Keywords: Prospect theory, Loss aversion, Reference point, Mixture aversion, Betweenness
    JEL: D11 D81 D91
    Date: 2023–02
  3. By: Wiese, Juliane V. (Warwick Business School); Powdthavee, Nattavudh (Nanyang Technological University, Singapore); Yeo, Jonathan (Nanyang Technological University, Singapore); Riyanto, Yohanes E. (Nanyang Technological University, Singapore)
    Abstract: How do we persuade people to part with money they feel they have rightly earned? We conducted a dyadic experiment (N=1, 986) where luck determined which of the players' performance counted toward winning the game. Despite luck playing a large part, we found strong evidence of justified deservingness among the winners. The better they performed in the task, the less they redistributed to their nonwinning partner. However, in treatments where performance was transparent, winners significantly increased redistribution to nonwinners who performed similarly well. We find that transparency can effectively alter redistributive preferences even when people feel fully deserving of their income.
    Keywords: luck, efforts, survivalship bias, redistribution, inequality, deservingness
    JEL: C9 D9
    Date: 2023–01
  4. By: Shoshan, Vered; Hazan, Tamir; Plonsky, Ori (Technion - Israel Institute of Technology)
    Abstract: In this paper, we propose a behavioral model called BEAST-Net, which combines the basic logic of BEAST, a psychological theory-based behavioral model, with machine learning (ML) techniques. Our approach is to formalize BEAST mathematically as a differentiable function and parameterize it with a neural network, enabling us to learn the model parameters from data and optimize it using backpropagation. The resulting model, BEAST-Net, is able to scale to larger datasets and adapt to new data with greater ease, while retaining the psychological insights and interpretability of the original model. We evaluate BEAST-Net on the largest public benchmark dataset of human choice tasks and show that it outperforms several baselines, including the original BEAST model. Furthermore, we demonstrate that our model can be used to provide interpretable explanations for choice behavior, allowing us to derive new psychological insights from the data. Our work makes a significant contribution to the field of human decision making by showing that ML techniques can be used to improve the scalability and adaptability of psychological theory based models while preserving their interpretability and ability to provide insights.
    Date: 2023–01–30

This nep-cbe issue is ©2023 by Marco Novarese. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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